Application of Dual-Source Modal Dispersion and Variational Bayesian Monte Carlo Method for Local Geoacoustic Inversion in Weakly Range-Dependent Shallow Water

Abstract

Most of the continental shelf area is a weakly range-dependent shallow-water environment. Compared with range-independent Bayesian geoacoustic inversion, range-dependent inversion usually has problems with the complex forward model and low efficiency for posterior analysis. According to the adiabatic normal-mode theory, the weakly range-dependent shallow-water environment can be divided into a series of range-independent segments; thus, this paper proposes a dual-source modal dispersion inversion method for local geoacoustic parameters of a segment based on a range-independent forward model. In addition, considering that the computational cost of the forward model limits the application of sampling-based methods for posterior analysis, a novel approximate variational inference, namely variational Bayesian Monte Carlo, is applied in this study. It has superior efficiency and shows similar accuracy compared with Markov Chain Monte Carlo sampling. This work is demonstrated in the shallow-water experiment in the continental shelf area of the East China Sea, and the results indicate that the local and range-dependent geoacoustic parameters are well-estimated.